Abstract

We present a novel almost-unsupervised approach to the task of Word Sense Disambiguation (WSD). We build sense examples automatically, using large quantities of Chinese text, and English-Chinese and Chinese-English bilingual dictionaries, taking advantage of the observation that mappings between words and meanings are often different in typologically distant languages. We train a classifier on the sense examples and test it on a gold standard English WSD dataset. The evaluation gives results that exceed previous state-of-the-art results for comparable systems. We also demonstrate that a little manual effort can improve the quality of sense examples, as measured by WSD accuracy. The performance of the classifier on WSD also improves as the number of training sense examples increases.